Princesses, Pirates, and an Empress: Dressing the 18th Century Anachronistically on Screen
Bibliographic record
Abstract
The costume designer for the Marie Antoinette (1938), Gilbert Adrian, researched the 18th century dress extensively, yet cut dress trimmings on bias, had shoulders revealed in court dresses, and wigs that were more 19th century than 18th. Marie Antoinette (2006) has more or less the same level of accuracy. “Anachronisms are found in almost every motion picture that portrays another period,” says costume and textile historian Edward Maeder, “… instead these costumes take elements of past styles and combine them with aspects of contemporary fashion,” (Landis, 2013). When the internet is filled with takedowns of the historical inaccuracies in films and TV, on screen depictions of the past continue to be anachronistic. Anachronisms are not always due to a lack of research, but sometimes quite the opposite! Good costume designers research their given period thoroughly, and, much like Maeder said, choose what to keep and what to change. Often these reasons are to better connect with modern sensibilities and styles, but these changes can also be done to elevate a piece of media into historical fantasy, or to say more about the themes and characters than any piece of completely historical costume could hope to do. With a focus on costume, three different pieces of recent media will be analysed: Marie Antoinette (2006) The Great (2020—) and Our Flag Means Death (2022—) and used as evidence of successful anachronistic costume that highlights their stories and characters
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".